Metaheuristic parameter tuning dateset

All thee metaheuristic algorithms (Ant Colony Optimization, Evolutionary Strategy, Imperialist Competitive Algorithm) contain multiple numerical parameters, that can be tuned to increase the efficiency of the search. These parameters are summarised below. Candidate configurations are generated by dividing each parameter into discrete sets - Full Fractional Design (FDD) approach. This creates a total of 12,000 candidate configurations for ACO, 1000 for ES and 5760 for ICA, a total of 18,760 across all algorithms.<b>ACO: </b>Parallel instances; Number of ants; Relative pheromone strength; relative heuristic information strength; exploitation to exploration ratio; cunning rate<b>ES:</b> Population size; mutation rate; number of local iterations; swap ratio<b>ICA:</b> Number of countries, imperialist ratio; number of local iterations; assimilation rate; average power of empire's colonies; independence rate<br><br>Dataset divided into three worksheets, one for each algorithm. For each configuration 10 run fitnesses are provided as well as average. With NaN representing values that did not finish within 60 seconds.